Can I see the full context of a bug report through my agent?

Yes. Use the `get_issue_details` tool with a specific Issue ID or UUID. Your agent will retrieve the full description, assigned labels, and current status, helping you understand the technical requirements instantly.

How do I check the progress of the current sprint cycle?

The `list_sprint_cycles` tool allows your agent to retrieve current iteration boundaries and temporal limits. You can see which cycles are active and how many issues are assigned to each team grouping.

Can my agent list all members of a specific team in Linear?

Absolutely. Use the `list_workspace_members` tool to identify registered users. Your agent will return precise organizational mappings and IDs, making it easy to route assignment requests through natural conversation.

Linear (Issue Tracking & PM) MCP Server for OpenAI Agents SDK 8 tools — connect in under 2 minutes

Name: Linear (Issue Tracking & PM)
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 8 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Linear (Issue Tracking & PM) through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Linear (Issue Tracking & PM) Assistant",
            instructions=(
                "You help users interact with Linear (Issue Tracking & PM). "
                "You have access to 8 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Linear (Issue Tracking & PM)"
        )
        print(result.final_output)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Linear (Issue Tracking & PM) MCP Server

Connect your Linear workspace to any AI agent and take full control of your issue tracking and product development lifecycle through natural conversation.

The OpenAI Agents SDK auto-discovers all 8 tools from Linear (Issue Tracking & PM) through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries Linear (Issue Tracking & PM), another analyzes results, and a third generates reports, all orchestrated through the Vinkius.

What you can do

Issue Orchestration — List and retrieve recent issues from your workspace, including their exact workflow states and assignee tracking directly from your agent
Deep Context Inspection — Pinpoint specific issues to extract full descriptions, assigned labels, and internal priority levels for rapid status updates
Project Monitoring — List all active mapped projects and track their organizational scopes, active state flags, and timeline limits securely
Sprint & Cycle Audit — Monitor current tracking sprint cycle bounds and temporal limits to understand team progress across active iteration loops
Team Management — Enumerate all logical team boundaries and workspace members to route operational assignments and project scopes efficiently
Workflow Taxonomy — Discover global metadata tags and labels used to categorize issues, ensuring your AI agent understands your internal organization rules

The Linear (Issue Tracking & PM) MCP Server exposes 8 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Linear (Issue Tracking & PM) to OpenAI Agents SDK via MCP

Follow these steps to integrate the Linear (Issue Tracking & PM) MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 8 tools from Linear (Issue Tracking & PM)

Why Use OpenAI Agents SDK with the Linear (Issue Tracking & PM) MCP Server

OpenAI Agents SDK provides unique advantages when paired with Linear (Issue Tracking & PM) through the Model Context Protocol.

Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Linear (Issue Tracking & PM) + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Linear (Issue Tracking & PM) MCP Server delivers measurable value.

Automated workflows: build agents that query Linear (Issue Tracking & PM), process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents — one queries Linear (Issue Tracking & PM), another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Linear (Issue Tracking & PM) tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Linear (Issue Tracking & PM) to resolve tickets, look up records, and update statuses without human intervention

Linear (Issue Tracking & PM) MCP Tools for OpenAI Agents SDK (8)

These 8 tools become available when you connect Linear (Issue Tracking & PM) to OpenAI Agents SDK via MCP:

get_issue

Get deep context for a specific identified Linear issue tracking limit

get_viewer

Get active authenticated mapping validating explicit global User boundaries

list_cycles

List current tracking sprint cycle bounds mapping start/end limits

list_issues

List recent issues mapped on Linear workspace

list_labels

List global string metadata tags bounding issue categorization logic

list_projects

List all explicit active mapped projects available in the workspace

list_teams

List all logical team segment boundaries mapping workspace access

list_users

List all explicitly mapped workspace members validating active access limits

Example Prompts for Linear (Issue Tracking & PM) in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Linear (Issue Tracking & PM) immediately.

"List all active issues assigned to me in the 'Engineering' team"

"Show me the details for issue 'ENG-101'"

"What is the end date for the current sprint cycle?"

Troubleshooting Linear (Issue Tracking & PM) MCP Server with OpenAI Agents SDK

Common issues when connecting Linear (Issue Tracking & PM) to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Linear (Issue Tracking & PM) + OpenAI Agents SDK FAQ

Common questions about integrating Linear (Issue Tracking & PM) MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.